Adversarial generation of gene expression data
暂无分享,去创建一个
Pietro Liò | Kevin Bryson | Helena Andrés-Terré | Ramon Viñas | P. Lio’ | K. Bryson | Helena Andrés-Terré | Ramón Viñas
[1] Ronald W. Davis,et al. Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.
[2] Kathleen Marchal,et al. SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms , 2006, BMC Bioinformatics.
[3] Julio Collado-Vides,et al. RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions , 2005, Nucleic Acids Res..
[4] Ralf Zimmer,et al. A Turing test for artificial expression data , 2013, Bioinform..
[5] B. Williams,et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.
[6] T. Speed,et al. Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.
[7] G. Cooper. Cells As Experimental Models , 2000 .
[8] Ian J. Goodfellow,et al. NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.
[9] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[10] Paul P. Wang,et al. Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..
[11] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[12] Dario Floreano,et al. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..
[13] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[14] Jeremiah J. Faith,et al. Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata , 2007, Nucleic Acids Res..
[15] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[16] E. Boersma,et al. Prevention of Catheter-Related Bacteremia with a Daily Ethanol Lock in Patients with Tunnelled Catheters: A Randomized, Placebo-Controlled Trial , 2010, PloS one.
[17] R. Sokal,et al. THE COMPARISON OF DENDROGRAMS BY OBJECTIVE METHODS , 1962 .
[18] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[19] Chris Wiggins,et al. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.
[20] Kevin Y. Yip,et al. Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data , 2010, PloS one.
[21] S. Busby,et al. Global regulators of transcription in Escherichia coli: mechanisms of action and methods for study. , 2008, Advances in applied microbiology.
[22] Fabio Rinaldi,et al. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond , 2015, Nucleic Acids Res..